Overview

Dataset statistics

Number of variables15
Number of observations172295
Missing cells279
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.7 MiB
Average record size in memory120.0 B

Variable types

Text1
Categorical2
Numeric10
DateTime2

Alerts

type_de_station has constant value "ISS"Constant
direction_du_vecteur_de_rafale_de_vent_max is highly overall correlated with direction_du_vecteur_de_vent_max and 1 other fieldsHigh correlation
direction_du_vecteur_de_vent_max is highly overall correlated with direction_du_vecteur_de_rafale_de_vent_max and 1 other fieldsHigh correlation
direction_du_vecteur_vent_moyen is highly overall correlated with direction_du_vecteur_de_rafale_de_vent_max and 1 other fieldsHigh correlation
force_moyenne_du_vecteur_vent is highly overall correlated with force_rafale_maxHigh correlation
force_rafale_max is highly overall correlated with force_moyenne_du_vecteur_ventHigh correlation
humidite is highly overall correlated with temperatureHigh correlation
pluie is highly overall correlated with pluie_intensite_maxHigh correlation
pluie_intensite_max is highly overall correlated with pluieHigh correlation
temperature is highly overall correlated with humiditeHigh correlation
id is highly imbalanced (99.6%)Imbalance
pluie is highly skewed (γ1 = 34.13787868)Skewed
data has unique valuesUnique
humidite has 2255 (1.3%) zerosZeros
direction_du_vecteur_de_vent_max has 152779 (88.7%) zerosZeros
pluie_intensite_max has 166879 (96.9%) zerosZeros
direction_du_vecteur_vent_moyen has 158848 (92.2%) zerosZeros
pluie has 167675 (97.3%) zerosZeros
direction_du_vecteur_de_rafale_de_vent_max has 152779 (88.7%) zerosZeros
force_moyenne_du_vecteur_vent has 127404 (73.9%) zerosZeros
force_rafale_max has 78256 (45.4%) zerosZeros

Reproduction

Analysis started2026-02-19 21:56:10.360575
Analysis finished2026-02-19 21:56:30.394832
Duration20.03 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

data
Text

Unique 

Distinct172295
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2026-02-19T22:56:30.602280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.998862
Min length1

Characters and Unicode

Total characters4134884
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172295 ?
Unique (%)100.0%

Sample

1st row543f7bce8e8800000c200c00
2nd row543f7bee8a8800000c001400
3rd row544170ce669800000c201400
4th row543f7c0e8a8810000c200c40
5th row544170ae829800000c201800
ValueCountFrequency (%)
5599106e229000002d8034001
 
< 0.1%
545376aea26000002c2038001
 
< 0.1%
543f7bce8e8800000c200c001
 
< 0.1%
543f7bee8a8800000c0014001
 
< 0.1%
544170ce669800000c2014001
 
< 0.1%
543f7c0e8a8810000c200c401
 
< 0.1%
544170ae829800000c2018001
 
< 0.1%
5453782eda2800006c404c001
 
< 0.1%
5453790ed62000602c9240001
 
< 0.1%
54537a6e8e5800004ce054001
 
< 0.1%
Other values (172285)172285
> 99.9%
2026-02-19T22:56:30.824571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
01721758
41.6%
5394835
 
9.5%
4256171
 
6.2%
8232051
 
5.6%
2210398
 
5.1%
c178042
 
4.3%
1167547
 
4.1%
6162148
 
3.9%
d136483
 
3.3%
e129744
 
3.1%
Other values (9)545707
 
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)4134884
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01721758
41.6%
5394835
 
9.5%
4256171
 
6.2%
8232051
 
5.6%
2210398
 
5.1%
c178042
 
4.3%
1167547
 
4.1%
6162148
 
3.9%
d136483
 
3.3%
e129744
 
3.1%
Other values (9)545707
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4134884
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01721758
41.6%
5394835
 
9.5%
4256171
 
6.2%
8232051
 
5.6%
2210398
 
5.1%
c178042
 
4.3%
1167547
 
4.1%
6162148
 
3.9%
d136483
 
3.3%
e129744
 
3.1%
Other values (9)545707
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4134884
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01721758
41.6%
5394835
 
9.5%
4256171
 
6.2%
8232051
 
5.6%
2210398
 
5.1%
c178042
 
4.3%
1167547
 
4.1%
6162148
 
3.9%
d136483
 
3.3%
e129744
 
3.1%
Other values (9)545707
 
13.2%

id
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing19
Missing (%)< 0.1%
Memory size1.3 MiB
42.0
172186 
1.0
 
89
0.0
 
1

Length

Max length4
Median length4
Mean length3.9994776
Min length3

Characters and Unicode

Total characters689014
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row42.0
2nd row42.0
3rd row42.0
4th row42.0
5th row42.0

Common Values

ValueCountFrequency (%)
42.0172186
99.9%
1.089
 
0.1%
0.01
 
< 0.1%
(Missing)19
 
< 0.1%

Length

2026-02-19T22:56:30.885512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-19T22:56:30.931887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
42.0172186
99.9%
1.089
 
0.1%
0.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0172277
25.0%
.172276
25.0%
4172186
25.0%
2172186
25.0%
189
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)689014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0172277
25.0%
.172276
25.0%
4172186
25.0%
2172186
25.0%
189
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)689014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0172277
25.0%
.172276
25.0%
4172186
25.0%
2172186
25.0%
189
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)689014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0172277
25.0%
.172276
25.0%
4172186
25.0%
2172186
25.0%
189
 
< 0.1%

humidite
Real number (ℝ)

High correlation  Zeros 

Distinct92
Distinct (%)0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean65.287999
Minimum0
Maximum97
Zeros2255
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2026-02-19T22:56:30.991877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31
Q154
median68
Q381
95-th percentile89
Maximum97
Range97
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.06624
Coefficient of variation (CV)0.29203285
Kurtosis0.62426158
Mean65.287999
Median Absolute Deviation (MAD)13
Skewness-0.87946088
Sum11247490
Variance363.52152
MonotonicityNot monotonic
2026-02-19T22:56:31.072178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
874830
 
2.8%
864469
 
2.6%
854273
 
2.5%
883918
 
2.3%
843902
 
2.3%
813895
 
2.3%
683870
 
2.2%
673820
 
2.2%
793784
 
2.2%
693770
 
2.2%
Other values (82)131744
76.5%
ValueCountFrequency (%)
02255
1.3%
76
 
< 0.1%
811
 
< 0.1%
910
 
< 0.1%
1043
 
< 0.1%
1147
 
< 0.1%
1270
 
< 0.1%
1382
 
< 0.1%
1484
 
< 0.1%
15110
 
0.1%
ValueCountFrequency (%)
976
 
< 0.1%
96155
 
0.1%
95443
 
0.3%
94502
 
0.3%
93841
 
0.5%
921431
 
0.8%
911984
1.2%
902427
1.4%
893086
1.8%
883918
2.3%

direction_du_vecteur_de_vent_max
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.93581193
Minimum0
Maximum15
Zeros152779
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2026-02-19T22:56:31.130810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.8688636
Coefficient of variation (CV)3.0656412
Kurtosis7.8484718
Mean0.93581193
Median Absolute Deviation (MAD)0
Skewness3.0283198
Sum161217
Variance8.2303784
MonotonicityNot monotonic
2026-02-19T22:56:31.184408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0152779
88.7%
92766
 
1.6%
102576
 
1.5%
112417
 
1.4%
122055
 
1.2%
81954
 
1.1%
71535
 
0.9%
31390
 
0.8%
21342
 
0.8%
4780
 
0.5%
Other values (6)2681
 
1.6%
ValueCountFrequency (%)
0152779
88.7%
1504
 
0.3%
21342
 
0.8%
31390
 
0.8%
4780
 
0.5%
5438
 
0.3%
6453
 
0.3%
71535
 
0.9%
81954
 
1.1%
92766
 
1.6%
ValueCountFrequency (%)
15228
 
0.1%
14379
 
0.2%
13679
 
0.4%
122055
1.2%
112417
1.4%
102576
1.5%
92766
1.6%
81954
1.1%
71535
0.9%
6453
 
0.3%

pluie_intensite_max
Real number (ℝ)

High correlation  Zeros 

Distinct13
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.0067508344
Minimum0
Maximum3
Zeros166879
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2026-02-19T22:56:31.234841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.042965844
Coefficient of variation (CV)6.3645234
Kurtosis530.49763
Mean0.0067508344
Median Absolute Deviation (MAD)0
Skewness14.806127
Sum1163
Variance0.0018460637
MonotonicityNot monotonic
2026-02-19T22:56:31.289205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0166879
96.9%
0.25226
 
3.0%
0.484
 
< 0.1%
0.629
 
< 0.1%
0.826
 
< 0.1%
19
 
< 0.1%
1.26
 
< 0.1%
1.66
 
< 0.1%
1.43
 
< 0.1%
23
 
< 0.1%
Other values (3)4
 
< 0.1%
(Missing)20
 
< 0.1%
ValueCountFrequency (%)
0166879
96.9%
0.25226
 
3.0%
0.484
 
< 0.1%
0.629
 
< 0.1%
0.826
 
< 0.1%
19
 
< 0.1%
1.26
 
< 0.1%
1.43
 
< 0.1%
1.66
 
< 0.1%
23
 
< 0.1%
ValueCountFrequency (%)
31
 
< 0.1%
2.61
 
< 0.1%
2.22
 
< 0.1%
23
 
< 0.1%
1.66
 
< 0.1%
1.43
 
< 0.1%
1.26
 
< 0.1%
19
 
< 0.1%
0.826
< 0.1%
0.629
< 0.1%

pression
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean99880.388
Minimum90000
Maximum102500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2026-02-19T22:56:31.360510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum90000
5-th percentile98400
Q199700
median100200
Q3100600
95-th percentile101400
Maximum102500
Range12500
Interquartile range (IQR)900

Descriptive statistics

Standard deviation1871.8448
Coefficient of variation (CV)0.018740865
Kurtosis19.960246
Mean99880.388
Median Absolute Deviation (MAD)400
Skewness-4.2821746
Sum1.7206894 × 1010
Variance3503803
MonotonicityNot monotonic
2026-02-19T22:56:31.442609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10010011102
 
6.4%
10030010941
 
6.4%
10020010529
 
6.1%
10040010516
 
6.1%
10050010480
 
6.1%
10000010064
 
5.8%
999009309
 
5.4%
1006008725
 
5.1%
998007868
 
4.6%
1007007511
 
4.4%
Other values (50)75230
43.7%
ValueCountFrequency (%)
900005070
2.9%
967007
 
< 0.1%
9680024
 
< 0.1%
9690059
 
< 0.1%
9700055
 
< 0.1%
9710082
 
< 0.1%
9720085
 
< 0.1%
97300121
 
0.1%
97400137
 
0.1%
97500157
 
0.1%
ValueCountFrequency (%)
10250012
 
< 0.1%
10240065
 
< 0.1%
10230050
 
< 0.1%
102200229
 
0.1%
102100360
 
0.2%
102000383
 
0.2%
101900906
0.5%
101800998
0.6%
101700964
0.6%
1016001388
0.8%

direction_du_vecteur_vent_moyen
Real number (ℝ)

High correlation  Zeros 

Distinct181
Distinct (%)0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11.840546
Minimum0
Maximum360
Zeros158848
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2026-02-19T22:56:31.522256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile108
Maximum360
Range360
Interquartile range (IQR)0

Descriptive statistics

Standard deviation47.986465
Coefficient of variation (CV)4.0527241
Kurtosis20.488967
Mean11.840546
Median Absolute Deviation (MAD)0
Skewness4.4854132
Sum2039830
Variance2302.7008
MonotonicityNot monotonic
2026-02-19T22:56:31.599383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0158848
92.2%
224648
 
0.4%
202633
 
0.4%
66546
 
0.3%
246546
 
0.3%
44470
 
0.3%
180429
 
0.2%
90331
 
0.2%
156307
 
0.2%
270234
 
0.1%
Other values (171)9283
 
5.4%
ValueCountFrequency (%)
0158848
92.2%
2111
 
0.1%
498
 
0.1%
6100
 
0.1%
871
 
< 0.1%
10111
 
0.1%
1276
 
< 0.1%
1484
 
< 0.1%
1670
 
< 0.1%
1883
 
< 0.1%
ValueCountFrequency (%)
36072
< 0.1%
35817
 
< 0.1%
35646
< 0.1%
35441
< 0.1%
35244
< 0.1%
35029
< 0.1%
34834
< 0.1%
34629
< 0.1%
34421
 
< 0.1%
34220
 
< 0.1%

type_de_station
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Memory size1.3 MiB
ISS
172275 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters516825
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowISS
2nd rowISS
3rd rowISS
4th rowISS
5th rowISS

Common Values

ValueCountFrequency (%)
ISS172275
> 99.9%
(Missing)20
 
< 0.1%

Length

2026-02-19T22:56:31.674114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-19T22:56:31.714697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
iss172275
100.0%

Most occurring characters

ValueCountFrequency (%)
S344550
66.7%
I172275
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)516825
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S344550
66.7%
I172275
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)516825
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S344550
66.7%
I172275
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)516825
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S344550
66.7%
I172275
33.3%

pluie
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct34
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.010815266
Minimum0
Maximum11.6
Zeros167675
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2026-02-19T22:56:31.757540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11.6
Range11.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.11289316
Coefficient of variation (CV)10.438315
Kurtosis2034.8876
Mean0.010815266
Median Absolute Deviation (MAD)0
Skewness34.137879
Sum1863.2
Variance0.012744866
MonotonicityNot monotonic
2026-02-19T22:56:31.827191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0167675
97.3%
0.22934
 
1.7%
0.4815
 
0.5%
0.6333
 
0.2%
0.8197
 
0.1%
199
 
0.1%
1.254
 
< 0.1%
1.436
 
< 0.1%
1.630
 
< 0.1%
1.820
 
< 0.1%
Other values (24)82
 
< 0.1%
(Missing)20
 
< 0.1%
ValueCountFrequency (%)
0167675
97.3%
0.22934
 
1.7%
0.4815
 
0.5%
0.6333
 
0.2%
0.8197
 
0.1%
199
 
0.1%
1.254
 
< 0.1%
1.436
 
< 0.1%
1.630
 
< 0.1%
1.820
 
< 0.1%
ValueCountFrequency (%)
11.61
< 0.1%
101
< 0.1%
9.21
< 0.1%
6.61
< 0.1%
6.41
< 0.1%
6.21
< 0.1%
62
< 0.1%
5.82
< 0.1%
5.61
< 0.1%
5.22
< 0.1%

direction_du_vecteur_de_rafale_de_vent_max
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean21.055768
Minimum0
Maximum337.5
Zeros152779
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2026-02-19T22:56:31.884311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile202.5
Maximum337.5
Range337.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64.549431
Coefficient of variation (CV)3.0656412
Kurtosis7.8484718
Mean21.055768
Median Absolute Deviation (MAD)0
Skewness3.0283198
Sum3627382.5
Variance4166.6291
MonotonicityNot monotonic
2026-02-19T22:56:31.939191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0152779
88.7%
202.52766
 
1.6%
2252576
 
1.5%
247.52417
 
1.4%
2702055
 
1.2%
1801954
 
1.1%
157.51535
 
0.9%
67.51390
 
0.8%
451342
 
0.8%
90780
 
0.5%
Other values (6)2681
 
1.6%
ValueCountFrequency (%)
0152779
88.7%
22.5504
 
0.3%
451342
 
0.8%
67.51390
 
0.8%
90780
 
0.5%
112.5438
 
0.3%
135453
 
0.3%
157.51535
 
0.9%
1801954
 
1.1%
202.52766
 
1.6%
ValueCountFrequency (%)
337.5228
 
0.1%
315379
 
0.2%
292.5679
 
0.4%
2702055
1.2%
247.52417
1.4%
2252576
1.5%
202.52766
1.6%
1801954
1.1%
157.51535
0.9%
135453
 
0.3%

force_moyenne_du_vecteur_vent
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.45681033
Minimum0
Maximum9
Zeros127404
Zeros (%)73.9%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2026-02-19T22:56:31.993186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.95564773
Coefficient of variation (CV)2.0920011
Kurtosis8.7280796
Mean0.45681033
Median Absolute Deviation (MAD)0
Skewness2.7195381
Sum78697
Variance0.91326258
MonotonicityNot monotonic
2026-02-19T22:56:32.040168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0127404
73.9%
125694
 
14.9%
210385
 
6.0%
34988
 
2.9%
42434
 
1.4%
5875
 
0.5%
6341
 
0.2%
7122
 
0.1%
830
 
< 0.1%
92
 
< 0.1%
(Missing)20
 
< 0.1%
ValueCountFrequency (%)
0127404
73.9%
125694
 
14.9%
210385
 
6.0%
34988
 
2.9%
42434
 
1.4%
5875
 
0.5%
6341
 
0.2%
7122
 
0.1%
830
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
830
 
< 0.1%
7122
 
0.1%
6341
 
0.2%
5875
 
0.5%
42434
 
1.4%
34988
 
2.9%
210385
 
6.0%
125694
 
14.9%
0127404
73.9%

force_rafale_max
Real number (ℝ)

High correlation  Zeros 

Distinct27
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.2458337
Minimum0
Maximum50
Zeros78256
Zeros (%)45.4%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2026-02-19T22:56:32.096199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile13
Maximum50
Range50
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.312385
Coefficient of variation (CV)1.3285909
Kurtosis4.1803817
Mean3.2458337
Median Absolute Deviation (MAD)2
Skewness1.8381099
Sum559176
Variance18.596664
MonotonicityNot monotonic
2026-02-19T22:56:32.156209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
078256
45.4%
321831
 
12.7%
517864
 
10.4%
216195
 
9.4%
613062
 
7.6%
88002
 
4.6%
105918
 
3.4%
133617
 
2.1%
142460
 
1.4%
161675
 
1.0%
Other values (17)3395
 
2.0%
ValueCountFrequency (%)
078256
45.4%
216195
 
9.4%
321831
 
12.7%
517864
 
10.4%
613062
 
7.6%
88002
 
4.6%
105918
 
3.4%
11735
 
0.4%
133617
 
2.1%
142460
 
1.4%
ValueCountFrequency (%)
501
 
< 0.1%
401
 
< 0.1%
381
 
< 0.1%
375
 
< 0.1%
355
 
< 0.1%
344
 
< 0.1%
3223
< 0.1%
3018
 
< 0.1%
2933
< 0.1%
2753
< 0.1%

temperature
Real number (ℝ)

High correlation 

Distinct485
Distinct (%)0.3%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14.522926
Minimum-50
Maximum42.6
Zeros109
Zeros (%)0.1%
Negative3720
Negative (%)2.2%
Memory size1.3 MiB
2026-02-19T22:56:32.221449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-50
5-th percentile2.9
Q19.5
median14.5
Q320.6
95-th percentile28.9
Maximum42.6
Range92.6
Interquartile range (IQR)11.1

Descriptive statistics

Standard deviation10.629002
Coefficient of variation (CV)0.73187745
Kurtosis15.357819
Mean14.522926
Median Absolute Deviation (MAD)5.5
Skewness-2.6511304
Sum2501937.1
Variance112.97569
MonotonicityNot monotonic
2026-02-19T22:56:32.298560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-502227
 
1.3%
11.71025
 
0.6%
121015
 
0.6%
11.31014
 
0.6%
9.51012
 
0.6%
11.8994
 
0.6%
11.5972
 
0.6%
10.2971
 
0.6%
11.6940
 
0.5%
12.2924
 
0.5%
Other values (475)161181
93.5%
ValueCountFrequency (%)
-502227
1.3%
-7.92
 
< 0.1%
-7.84
 
< 0.1%
-7.73
 
< 0.1%
-7.62
 
< 0.1%
-6.98
 
< 0.1%
-6.83
 
< 0.1%
-6.71
 
< 0.1%
-6.65
 
< 0.1%
-6.52
 
< 0.1%
ValueCountFrequency (%)
42.61
< 0.1%
42.41
< 0.1%
42.21
< 0.1%
41.91
< 0.1%
41.82
< 0.1%
41.62
< 0.1%
41.52
< 0.1%
41.42
< 0.1%
41.32
< 0.1%
41.21
< 0.1%
Distinct171945
Distinct (%)99.8%
Missing20
Missing (%)< 0.1%
Memory size1.3 MiB
Minimum2019-10-27 02:00:00+01:00
Maximum2026-02-19 22:30:00+01:00
Invalid dates97001
Invalid dates (%)56.3%
2026-02-19T22:56:32.459004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:32.544244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct171945
Distinct (%)99.8%
Missing20
Missing (%)< 0.1%
Memory size1.3 MiB
Minimum2019-06-06 00:00:00+00:00
Maximum2026-02-19 21:30:00+00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-19T22:56:32.629982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:32.711003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2026-02-19T22:56:28.679618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:21.577684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:22.328744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:23.110470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:23.878752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:24.733888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:25.535831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:26.275263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:27.151527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:27.907840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:28.749448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:21.652809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:22.402877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:23.184046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:23.954790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:24.809746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:25.605530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:26.354047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:27.222264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:27.983112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:28.825155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:21.731098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:22.482598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:23.264884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:24.037011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:24.892309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:25.683988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:26.436979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:27.302024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:28.062443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:28.900584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:21.806779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:22.558641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:23.340380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:24.116715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:24.970772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:25.758057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:26.519264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:27.378725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:28.142098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:28.981872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:21.883242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:22.639107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:23.419179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:24.193704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:25.053636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:25.835319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:26.602130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:27.456585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:28.220334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:29.058246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:21.961139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:22.717884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:23.501259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:24.352224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:25.146739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:25.911021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:26.684143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:27.532223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:28.301198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:29.130417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:22.034757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:22.795105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:23.575833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:24.430814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:25.223593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:25.981190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:26.762826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:27.609074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:28.376510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:29.208600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:22.111122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:22.877206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:23.653696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:24.511246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:25.306586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:26.058285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:26.843228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:27.690933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:28.459045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:29.286523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:22.186525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:22.951644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:23.728715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:24.586432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:25.383818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:26.128785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:26.920433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:27.762566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:28.533923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:29.358368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:22.257387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:23.028990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:23.805451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:24.661911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:25.462209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:26.201886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:26.998434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:27.837207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-19T22:56:28.605298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-19T22:56:32.770239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
direction_du_vecteur_de_rafale_de_vent_maxdirection_du_vecteur_de_vent_maxdirection_du_vecteur_vent_moyenforce_moyenne_du_vecteur_ventforce_rafale_maxhumiditeidpluiepluie_intensite_maxpressiontemperature
direction_du_vecteur_de_rafale_de_vent_max1.0001.0000.7280.0700.285-0.1470.049-0.028-0.012-0.0570.180
direction_du_vecteur_de_vent_max1.0001.0000.7280.0700.285-0.1470.049-0.028-0.012-0.0570.180
direction_du_vecteur_vent_moyen0.7280.7281.0000.0930.270-0.1680.020-0.016-0.011-0.0570.171
force_moyenne_du_vecteur_vent0.0700.0700.0931.0000.762-0.1150.019-0.0020.001-0.1610.093
force_rafale_max0.2850.2850.2700.7621.000-0.1380.0190.0100.018-0.1670.135
humidite-0.147-0.147-0.168-0.115-0.1381.0000.0580.1830.2020.094-0.638
id0.0490.0490.0200.0190.0190.0581.0000.0000.0000.1080.060
pluie-0.028-0.028-0.016-0.0020.0100.1830.0001.0000.921-0.117-0.095
pluie_intensite_max-0.012-0.012-0.0110.0010.0180.2020.0000.9211.000-0.120-0.101
pression-0.057-0.057-0.057-0.161-0.1670.0940.108-0.117-0.1201.000-0.123
temperature0.1800.1800.1710.0930.135-0.6380.060-0.095-0.101-0.1231.000

Missing values

2026-02-19T22:56:29.471826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-19T22:56:29.655581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-19T22:56:30.150088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

dataidhumiditedirection_du_vecteur_de_vent_maxpluie_intensite_maxpressiondirection_du_vecteur_vent_moyentype_de_stationpluiedirection_du_vecteur_de_rafale_de_vent_maxforce_moyenne_du_vecteur_ventforce_rafale_maxtemperatureheure_de_parisheure_utc
0543f7bce8e8800000c200c0042.081.00.00.099700.00.0ISS0.00.00.03.08.32026-02-01T00:15:00+01:002026-01-31T23:15:00+00:00
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